Felix C. H. Chan

Predicted Increases in Incidence of Colorectal Cancer in Developed and Developing Regions, in Association With Ageing Populations

Background & Aims Population growth and changes in demographic structure are linked to trends in colorectal cancer (CRC) incidence. The aim of this study is to estimate future CRC incidence in the ageing population, and compare trends across developing and developed regions. Methods Cancer and population data were extracted from the International Agency for Research on Cancer. Annual incidence rates for the major types of cancer in 118 selected populations were extracted from 102 cancer registries in 39 countries worldwide. We selected 8 jurisdictions (from the United States, Europe, and Asia) that reported 20‐year cancer incidence rates since 1988. Time series models were constructed to project cancer incidence, by sex and age, to 2030. Incidence rates for persons older than 65 years were combined and further adjusted for change of ageing population. We compared age‐adjusted incidence rates among the jurisdictions. Results The total population older than 65 years old was 12,917,794 in 1988, and the number increased by almost 40% to 17,950,115 in 2007. In developed countries in the West CRC incidence is predicted to decrease by 16.3% in the United States, increase by 4.8% in the United Kingdom, and increase by 4.7% in Sweden by 2030. In developing countries, such as China (Shanghai), Croatia, and Costa Rica, CRC incidence is predicted to increase in a steep curve by 2030 because of the growing population and ageing effect; in 2030, the incidence increases were 60.5% for China, 47.0% for Croatia, and 18.5% for Costa Rica. We also predict CRC incidence will increase greatly by 2030 in Japan and Hong Kong, which are developed regions. Conclusions With the exception of the United States, the incidence of CRC is expected to continue to rise in most regions in the coming decades, due to population growth and changes in demographic structure. The predicted increases are more marked in developing regions with limited health care resources.

Cancer burden with ageing population in urban regions in China: projection on cancer registry data from World Health Organization

Background China is facing the challenges of an expanding ageing population and the impact of rapid urbanization, cancer rates are subsequently increasing. This study focuses on the changes of the ageing population and projects the incidence of common ageing-related cancers in the urban regions in China up to 2030. Sources of data Cancer incidence data and population statistics in China were extracted from the International Agency for Research on Cancer. Areas of agreement Due to improving longevity in China, continuous and remarkable increasing trends for the lung, colorectal and prostate cancers are expected. Growing points The rate of expanding ageing population was taken into account when predicting the trend of cancer incidence; the estimations of ageing-related cancers were more factual and significant than using the conventional approach of age standardization. Areas timely for developing research The incidence rates of lung, colorectal and prostate cancers will continue to rise in the future decades due to the rise of ageing population. Lifestyle modification such as cutting tobacco smoking rates and promoting healthier diets as well as cancer screening programs should be a health system priority in order to decrease the growing burden of cancer-related mortality and morbidity.

Data Visualization on Global Trends on Cancer Incidence An Application of IBM Watson Analytics

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across over a hundred of cancer registries worldwide. In this study, we present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. We included 26 cancers from different geographic regions. An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, we can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is assessable by any computer connected to the Internet.

How can we better use Twitter to find a person who got lost due to dementia

Twitter is a social media platform for online message sharing. The aim of this study is to evaluate the effectiveness of using Twitter to search for people who got lost due to dementia. The online messages on Twitter, i.e., tweets, were collected through an Application Programming Interface. Contents of the tweets were analysed. The personal characteristics, features of tweets and types of Twitter users were collected to investigate their associations with whether a person can be found within a month. Logistic regression was used to identify the features that were useful in finding the missing people. Results showed that the young age of the persons with dementia who got lost, having tweets posted by police departments, and having tweets with photos can increase the chance of being found. Social media is reshaping the human communication pathway, which may lead to future needs on a new patient-care model.Social media: Twitter best practices for finding lost people with dementiaTwitter can help find people who get lost due to dementia—if the social media tool is used optimally. Kelvin Tsoi and colleagues from the Chinese University of Hong Kong analysed over 45,000 tweets about people with dementia or Alzheimer’s disease who went missing. The researchers identified 40 individuals who were found within a month of the first tweet, and 14 others who were not. Comparing the two groups, they found that people were more likely to be located if they were younger, if police departments were tweeting about the missing person, and if tweets contained photos of the lost individuals. The findings point toward better ways of using Twitter to track down those who get lost, a common and unpredictable phenomenon that occurs for at least half of all people living with dementia.

Risk of gastrointestinal bleeding and benefit from colorectal cancer reduction from long-term use of low-dose aspirin: A retrospective study of 612 509 patients: Long term aspirin: Bleeding or cancer

Aspirin, commonly used for prevention of cardiovascular and cerebrovascular diseases, is well known to protect against development of colorectal cancer (CRC) but increases risk of gastrointestinal bleeding (GIB). This cohort study aims to evaluate the benefit of low‐dose aspirin to prevent CRC and its associated risk of GIB.

Data Visualization with IBM Watson Analytics for Global Cancer Trends Comparison from World Health Organization

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across a hundred of cancer registries worldwide. In this study, the authors present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. They included 26 forms of cancers from eight different geographic regions which are United States, the United Kingdom, Costa Rica, Sweden, Croatia, Japan, Hong Kong and China (Shanghai). An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, one can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is accessible by any computer connected to the Internet. IBM Watson Analytics released a latest version named “IBM Watson Analytics New User Experience†in the end of 2016. The new version streamlined the process to add data, discover data meaning and display result visually. The authors discuss the new features in the end of this paper.

COMBINATION THERAPY SHOWED NO ADDITIONAL BENEFIT OVER MONOTHERAPY FOR ALZHEIMER’S DISEASE: A NETWORK META-ANALYSIS OF 76 RANDOMIZED CONTROLLED TRIALS

rus (MFG) seed location during task execution, over the course of each intervention phase, were calculated. Left: Task FC changes during treatment with placebo include a combination of increases (cool, green colors) and decreases (hot colors). Center: Task FC changes during treatment with the investigational product include mainly increases (cool colors). Right: Task FC between the MFG and locations mainly within the occipital cortex decreased significantly more during treatment with the investigational product than it did during treatment with placebo (hot colors). However, task FC between theMFG and locations within the cingulate cortex increased significantly more during treatment with Cerbella than it did during treatment with placebo (cool colors). All maps show differences over time that are significant at the voxel level at a level of p1⁄4.01 and a cluster significance threshold of p1⁄4.001 using AlphaSim cluster correction [1]. Poster Presentations: Wednesday, July 25, 2018 P1434

Personal Wearable Devices to Measure Heart Rate Variability: A Framework of Cloud Platform for Public Health Research

Background: Heart rate variability (HRV) refers to the variation in time interval between heart rates (RR-interval). Studies have demonstrated that emotional disorder is associated with lower HRV. Electrocardiography (ECG) is the conventional HRV measurement conducted by healthcare professionals. Wearable devices with HRV measurement function may be a convenient and low-cost alternative. This study aimed to evaluate the HRV results between a wearable device and ECG. Methods: Parents from disadvantaged families were recruited and requested to wear the wearable device, second generation of Microsoft Band (MS band), on their non-dominant hand and a 7-lead ECG simultaneously for 10 minutes. Mean RR-interval was used to measure the level of HRV; subject with mean RR-interval greater than 750ms was defined as normal. Sensitivity and specificity was used to quantify the consistence between the MS band and the ECG. Results: A total of 40 subjects were recruited. The mean RR-interval of ECG measurements ranged from 487.87 to 1076.5; 9 of them had abnormal RR-interval. The sensitivity and specificity of the MS band were 88.89% and 77.42% respectively. Conclusion: This study showed that wearable device was a reliable instrument for HRV measurement in static posture. Further investigations should look into the accuracy during motion.

Classification of Visit-to-Visit Blood Pressure Variability: A Machine Learning Approach for Data Clustering on Systolic Blood Pressure Intervention Trial (SPRINT)

Background: Blood pressure variability (BPV) is associated with the cardiovascular disease. However, there is no standard risk stratification method to evaluate BPV. Our study aims to cluster BPV into three levels, namely, low, medium and high levels, by a machine learning approach. Methods: The Systolic Blood Pressure Intervention Trial (SPRINT) dataset, which includes patients with hypertension or at risk of cardiovascular diseases, was obtained from a clinical data sharing platform. In the clinical trial, participants with systolic blood pressure (SBP) of at least 130 mmHg and an increased cardiovascular risk were randomized to receive intensive treatment (targeting SBP below 120 mmHg) or standard treatment (targeting SBP below 140 mmHg), and blood pressure (BP) were measured and recorded during the follow-up periods. Visit-to-visit BPV was measured by the deviation between the observed records and the personalized BP trends, and two-dimensional clustering on SBP and diastolic BP were applied. Different curve fitting techniques (linear regression and cubic regression) and clustering methods (K-means and Agglomerative Clustering) were attempted and compared with each other. Results: With 8,092 participants and a median follow-up of 3.26 years, linear regression was a simple and reliable method to capture the BP trend. K-means model showed stable data clustering results. Intensive treatment showed to be effective for participants with a high level of BPV. Conclusion: Machine learning can be used for data clustering on BPV.